Li Qian, Song Zuhua, Zhang Dan, Li Xiaojiao, Liu Qian, Yu Jiayi, Wen Youjia, Zhang Jiayan, Ren Xiaofang, Li Zongwen, Zhang Xiaodi, Tang Zhuoyue
Department of Radiology, Chongqing General Hospital, Chongqing, China.
Department of Clinical Science, Philips Healthcare, Chengdu, China.
Quant Imaging Med Surg. 2023 Jun 1;13(6):3428-3440. doi: 10.21037/qims-22-698. Epub 2023 Apr 6.
The misdiagnosis of papillary thyroid microcarcinoma (PTMC) and micronodular goiter (MNG) may lead to overtreatment and unnecessary medical expenditure by patients. This study developed and validated a dual-energy computed tomography (DECT)-based nomogram for the preoperative differential diagnosis of PTMC and MNG.
This retrospective study analyzed the data of 366 pathologically confirmed thyroid micronodules, of which 183 were PTMCs and 183 were MNGs, from 326 patients who underwent DECT examinations. The cohort was divided into the training (n=256) and validation cohorts (n=110). The conventional radiological features and DECT quantitative parameters were analyzed. The iodine concentration (IC), normalized iodine concentration (NIC), effective atomic number, normalized effective atomic number, and slope of the spectral attenuation curves in the arterial phase (AP) and venous phase (VP) were measured. A univariate analysis and stepwise logistic regression analysis were performed to screen the independent indicators for PTMC. A radiological model, DECT model, and DECT-radiological nomogram were constructed, and the performances of the 3 models were assessed using the receiver operating characteristic curve, DeLong test, and a decision curve analysis (DCA).
The IC in the AP [odds ratio (OR) =0.172], NIC in the AP (OR =0.003), punctate calcification (OR =2.163), and enhanced blurring (OR =3.188) were identified as independent predictors in the stepwise-logistic regression. The areas under the curve with 95% confidence intervals (CIs) of the radiological model, DECT model, and DECT-radiological nomogram were 0.661 (95% CI: 0.595-0.728), 0.856 (95% CI: 0.810-0.902), and 0.880 (95% CI: 0.839-0.921), respectively, in the training cohort; and 0.701 (95% CI: 0.601-0.800), 0.791 (95% CI: 0.704-0.877), and 0.836 (95% CI: 0.760-0.911), respectively, in the validation cohort. The diagnostic performance of the DECT-radiological nomogram was better than that of the radiological model (P<0.05). The DECT-radiological nomogram was found to be well calibrated and had a good net benefit.
DECT provides valuable information for differentiating between PTMC and MNG. The DECT-radiological nomogram could serve as an easy-to-use, noninvasive, and effective method for differentiating between PTMC and MNG and help clinicians in decision-making.
甲状腺微小乳头状癌(PTMC)和微小结节性甲状腺肿(MNG)的误诊可能导致过度治疗以及患者不必要的医疗支出。本研究开发并验证了一种基于双能计算机断层扫描(DECT)的列线图,用于PTMC和MNG的术前鉴别诊断。
这项回顾性研究分析了326例行DECT检查患者的366个经病理证实的甲状腺微小结节的数据,其中183个为PTMC,183个为MNG。该队列被分为训练组(n = 256)和验证组(n = 110)。分析了传统放射学特征和DECT定量参数。测量了动脉期(AP)和静脉期(VP)的碘浓度(IC)、归一化碘浓度(NIC)、有效原子序数、归一化有效原子序数以及光谱衰减曲线的斜率。进行单因素分析和逐步逻辑回归分析以筛选PTMC的独立指标。构建了放射学模型、DECT模型和DECT - 放射学列线图,并使用受试者操作特征曲线、德龙检验和决策曲线分析(DCA)评估这3种模型的性能。
逐步逻辑回归确定AP期的IC(比值比[OR]=0.172)、AP期的NIC(OR = 0.003)、点状钙化(OR = 2.163)和强化模糊(OR = 3.188)为独立预测因素。训练组中,放射学模型、DECT模型和DECT - 放射学列线图的曲线下面积及95%置信区间(CI)分别为0.661(95%CI:0.595 - 0.728)、0.856(95%CI:0.810 - 0.902)和0.880(95%CI:0.839 - 0.921);验证组中分别为0.701(95%CI:0.601 - 0.800)、0.791(95%CI:0.704 - 0.877)和0.836(95%CI:0.760 - 0.911)。DECT - 放射学列线图的诊断性能优于放射学模型(P<0.05)。发现DECT - 放射学列线图校准良好且净效益良好。
DECT为区分PTMC和MNG提供了有价值的信息。DECT - 放射学列线图可作为一种易于使用、无创且有效的方法来区分PTMC和MNG,并帮助临床医生进行决策。